International Conference on Computer and Knowledge Engineering

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Adaptive Hybrid TRCA–CORRCA algorithm for enhanced accuracy in SSVEP-based brain-computer interfaces

Authors :

Sepehr Tayebeh Khabbaz¹ Sina Tayebeh Khabbaz² Arshia Barani³ Arsalan Ganjeh⁴ Sasan Harifi⁵ Seyed Mohsen Mirhosseini⁶

1- Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran 2- Department of Computer Science, University of Mazandaran, Babolsar, Iran 3- Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran 4- Department of Computer Science, University of Mazandaran, Babolsar, Iran 5- Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran 6- Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran

Keywords :

Brain-Computer Interface،SSVEP،Adaptive Hybrid Algorithm،TRCA،CORRCA،Pattern Recognition

Abstract :

This paper introduces a novel approach to improve Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interfaces (BCIs) with the Adaptive Hybrid TRCA–CORRCA (AH-TC) algorithm. By dynamic fusion of Task-Related Component Analysis (TRCA) and Correlated Component Analysis (CORRCA), AH-TC enhances robustness and accuracy and adapts to signal condition variations. With a Signal–Noise Confidence Index (SNCI), the algorithm dynamically balances TRCA and CORRCA for optimal decoding performance. Experimental results show that AH-TC achieves 85.23% accuracy at 0.9 seconds, superior to TRCA (77.32%), CORRCA (70.48%), and CCA (23.54%). AH-TC also sustains excellent Information Transfer Rate (ITR) performance of 270.04 bits/min at 0.5s, reflecting strong performance under real-time scenarios. The method offers a promising direction for next-generation BCI systems with low-calibration, high-performance, and real-time capabilities.